Combining Segmentation And Tracking For The Classification Of Moving Objects In Video Scenes

This paper describes a method of detecting, tracking and identifying moving objects in video scenes. The method is based on an adaptive change detector which detects and tracks moving objects and extracts silhouettes of the objects from the background so they can be classified by shape. The adaptive change detector uses estimates of image noise and contrast to dynamically adjust decision thresholds. A principal feature of this method is the synergistic interaction between the tracker, the segmenter and the classifier to eliminate uninteresting objects, to improve estimates of the background and noise, to guide threshold selection, and to influence feature selection for classification.